The disclosed embodiments relate generally to data processing systems, and more specifically to techniques for performing non-event pattern matching on continuous event streams using variable duration functionality.
In traditional database systems, data is stored in one or more databases usually in the form of tables. The stored data is then queried and manipulated using a data management language such as SQL. For example, a SQL query may be defined and executed to identify relevant data from the data stored in the database. A SQL query is thus executed on a finite set of data stored in the database. Further, when a SQL query is executed, it is executed once on the finite data set and produces a finite static result. Databases are thus best equipped to run queries over finite stored data sets.
A number of modern applications and systems however generate data in the form of continuous data or event streams instead of a finite data set. Examples of such applications include but are not limited to sensor data applications, financial tickers, network performance measuring tools (e.g. network monitoring and traffic management applications), clickstream analysis tools, automobile traffic monitoring, and the like. For example, a temperature sensor may be configured to send out temperature readings. Such applications have given rise to a need for a new breed of applications that can process the data streams.
Managing and processing data for these types of event stream-based applications involves building data management and querying capabilities with a strong temporal focus. A different kind of querying mechanism is needed that comprises long-running queries over continuous unbounded sets of data. While some vendors now offer product suites geared towards event streams processing, these product offerings still lack the processing flexibility required for handling today's events processing needs.